Deploy to any Linux-based development board
Edge Impulse for Linux lets you run your models on any Linux-based development board,
with SDKs for Node.js, Python, Go and C++ to integrate your models quickly into
your application.
- Install the Edge Impulse Linux CLI
- Run
edge-impulse-linux-runner (run with --clean to switch projects)
Run your model as a Docker container
To run your model as a container with an HTTP interface, use:
Container:
public.ecr.aws/g7a8t7v6/inference-container:4a3c501b5c8416a89057cefb2ffd693a589fc591
Arguments:
--api-key ei_cd6d509007901f6d773862392b85bdb6518c058161d0196a1214530cf2542d51 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container:4a3c501b5c8416a89057cefb2ffd693a589fc591 \
--api-key ei_cd6d509007901f6d773862392b85bdb6518c058161d0196a1214530cf2542d51 \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at
http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
Run your model as a Docker container
To run your model as a container with an HTTP interface on NVIDIA Jetson's GPUs (JetPack 4.6.x), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson:76bb5df9aca2e555fa8fc3a6d0ba966429ca1b87
Arguments:
--api-key ei_cd6d509007901f6d773862392b85bdb6518c058161d0196a1214530cf2542d51 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container-jetson:76bb5df9aca2e555fa8fc3a6d0ba966429ca1b87 \
--api-key ei_cd6d509007901f6d773862392b85bdb6518c058161d0196a1214530cf2542d51 \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at
http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
Run your model as a Docker container
To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 5.1.x), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:a37d6c66b940bc7cd1fa0423562e261cd129b855
Arguments:
--api-key ei_cd6d509007901f6d773862392b85bdb6518c058161d0196a1214530cf2542d51 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin:a37d6c66b940bc7cd1fa0423562e261cd129b855 \
--api-key ei_cd6d509007901f6d773862392b85bdb6518c058161d0196a1214530cf2542d51 \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at
http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
Run your model as a Docker container
To run your model as a container with an HTTP interface on NVIDIA Jetson Orin's GPUs (JetPack 6.0), use:
Container:
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:6f7f12bd33484ce64666c31744404a7bd55b0405
Arguments:
--api-key ei_cd6d509007901f6d773862392b85bdb6518c058161d0196a1214530cf2542d51 --run-http-server 1337
For example, in a one-liner locally:
docker run --rm -it --runtime=nvidia --gpus all \
-p 1337:1337 \
public.ecr.aws/g7a8t7v6/inference-container-jetson-orin-6-0:6f7f12bd33484ce64666c31744404a7bd55b0405 \
--api-key ei_cd6d509007901f6d773862392b85bdb6518c058161d0196a1214530cf2542d51 \
--run-http-server 1337
This automatically builds and downloads the latest model (incl. hardware optimizations), and runs an HTTP endpoint at
http://localhost:1337 with instructions.
Read the docs for information,
like bundling in your model inside the container and selecting extra hardware optimizations.
